Upcoming Datasets: Global wind map. A presentation by Jake Badger ( Risoe DTU) during the Global Atlas side event which held at the World Future Energy Summit in 2014

1. WFES 2014
EUDP
Global Wind Atlas:
New, Unique, and Dedicated
dataset for the
Global Atlas
DTU Wind Energy, Technical University of Denmark
Presented by Jake Badger
EUDP is a Danish fund for development and demonstration projects from the
Danish Energy Agency

2. Context
The global wind atlas objectives are to:
• provide wind resource data accounting for high resolution effects
• use microscale modelling to capture small scale wind speed variability
(crucial for better estimates of aggregated wind resource)
Suitable for aggregation and upscaling analysis and energy integration
analysis for energy planners and policy makers
WARNING: Not suitable for developers and site resource assessment
IPCC SRREN report: range tech. pot. 19 – 125 PWh /
year (onshore and near shore)
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DTU Wind Energy, Technical University of Denmark

3. Importance of resolution and microscale modelling
Wind resource (power density) calculated at different resolutions
50 km
10 km
5 km
50 km
324 W/m2
378 W/m2
2.5 km
323 W/m2
410 W/m2
mean power density of total area
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DTU Wind Energy, Technical University of Denmark
328 W/m2
398 W/m2
0.1 km
505 W/m2
641 W/m2
mean power density for windiest 50% of area

4. Importance of resolution
Mean wind power density for windiest half of area
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DTU Wind Energy, Technical University of Denmark

5. Importance of resolution
Note:
Even for Danish
landscape effect can
give 25 % boast in
wind resource at the
windiest 5 percentile.
Mean wind power density for 10% of area
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DTU Wind Energy, Technical University of Denmark

6. Input: newly available global dataset
Reanalysis: atmospheric data
Product
Model system
Horizontal
resolution
Period covered
Temporal
resolution
ERA Interim
reanalysis
T255, 60 vertical levels, 4DVar
~0.7° × 0.7°
1989-present
3- and 6hourly
NASA –
GAO/MERRA
GEOS5 data assimilation system
(Incremental Analysis Updates), 72
levels
0.5° × 0.67°
1979-present
3-hourly
NCAR CFDDA
MM5 (regional model)+ FDDA
~40 km
1985-2005
hourly
CFSR
NCEP GFS (global forecast system)
~38 km
1979-2009 (&
updating)
hourly
Topography: surface description
Elevation
Shuttle Radar Topography Mission (SRTM), version 2.1, released 2009
ASTER Global Digital Elevation Model (ASTER GDEM), version 1, released 2009
resolution 90 m
resolution 30 m
Land cover
ESA GlobCover, version 2.1, released 2008,
resolution 300 m
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DTU Wind Energy, Technical University of Denmark

7. Reanalysis data from NCEP DOE II 1980-2009
mean wind at 10 m direct from dataset
Wind speed shows variation in part due to changing surface roughness length.
• Tendency for lower winds over land, higher winds over sea.
• Sub-grid scale variation of orography and roughness will lead to marked variation in wind
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DTU Wind Energy, Technical University of Denmark

8. Reanalysis data from NCEP DOE II 1980-2009
generalized mean wind speed at 10 m and z0 = 10 cm
Wind speed shows less variation, roughness length is now 10 cm everywhere
• Less contrast between land and sea
• Generalized wind climate is the link to downscaling models
• described sectorwise, for different heights and different roughness lengths (WAsP libfile)
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DTU Wind Energy, Technical University of Denmark

9. The GWA jobs
• MGRS grid zones form basis
of the job structure
• MRGS grid zones are divided
into 4 pieces (total 4903)
• 2439 jobs required
DTU Wind Energy, Technical University of Denmark

10. Example jobs
DTU Wind Energy, Technical University of Denmark

11. Example jobs
DTU Wind Energy, Technical University of Denmark

12. EUDP Global Wind Atlas Output
Heights:
50, 100, 200 m
Weibull A and k for 12
direction sectors
Aggregated products based
on calculations at 250 m
grid spacing
Verification against
mesoscale existing
national wind atlases
Verification against SAR
offshore resource
estimation
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DTU Wind Energy, Technical University of Denmark

13. Application of high resolution resource data
Wind Atlas for South Africa (WASA) experience in planning
Credit: Cornelius van der Westhuizen, CSIR, South Africa
See also: www.windaba.co.za/wp-content/uploads/2013/10/Cornelius-van-derWesthuizen-Methodolody-and-initial-results-of-the-DEA-wind-SEA.pdf
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DTU Wind Energy, Technical University of Denmark

14. Application of high resolution resource data
• DTU PhD working on advanced GIS applications of Global Wind Atlas
– Conversion of high resolution wind climate data to technical potential
incorporating optimization.
• EU JRC project developing technical potential data for TIMES-EU, derived
from Global Wind Atlas
– Conversion and aggregation to formats for integrated assessment
modelling (IAM).
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DTU Wind Energy, Technical University of Denmark

15. Summary
To discover the true global wind resource and make it available for all
•
•
•
•
provide wind resource data accounting for high resolution effects
use a unified methodology using newer higher reanalysis datasets
verification and publication of the methodology are important
be applied for aggregation and upscaling analysis and energy
integration analysis for energy planners and policy makers
• Look out for 2nd end-user workshop late 2014.
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DTU Wind Energy, Technical University of Denmark

16. Thank you for listening
jaba@dtu.dk
Acknowledgement
This work is undertaken in collaboration with the Danish Energy Agency
and funded by grant EUDP 11-II, Globalt Vind Atlas, 64011-0347
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DTU Wind Energy, Technical University of Denmark

17. Project overview
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DTU Wind Energy, Technical University of Denmark

18. Calculation of local wind climates at microscale
Job Management Console
Job Creation
WAsP Worker
Results Exporter
DTU Wind Energy, Technical University of Denmark